:jddlmZddlmZmZmZddlmZmZddl m Z m Z ddl m Z ddlmZddlmZddlmZmZesd gZGd d e Zy ) )Sequence)AnyOptionalUnion)Tensortensor)_minkowski_distance_compute_minkowski_distance_update)Metric)TorchMetricsUserError)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzMinkowskiDistance.plotceZdZUdZdZeeed<dZeeed<dZ eeed<dZ e ed<e ed <d e d e d d ffd Zde de d d fdZd e fdZ ddeee ee fdeed efdZxZS)MinkowskiDistanceaCompute `Minkowski Distance`_. .. math:: d_{\text{Minkowski}} = \sum_{i}^N (| y_i - \hat{y_i} |^p)^\frac{1}{p} where :math: `y` is a tensor of target values, :math: `\hat{y}` is a tensor of predictions, :math: `\p` is a non-negative integer or floating-point number This metric can be seen as generalized version of the standard euclidean distance which corresponds to minkowski distance with p=2. Args: p: int or float larger than 1, exponent to which the difference between preds and target is to be raised kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.regression import MinkowskiDistance >>> target = tensor([1.0, 2.8, 3.5, 4.5]) >>> preds = tensor([6.1, 2.11, 3.1, 5.6]) >>> minkowski_distance = MinkowskiDistance(3) >>> minkowski_distance(preds, target) tensor(5.1220) Tis_differentiableFhigher_is_betterfull_state_updateplot_lower_boundminkowski_dist_sumpkwargsreturnNc t|di|t|ttfr|dk\st d|||_|jdtddy)Nz>Argument ``p`` must be a float or int greater than 1, but got rrsum)defaultdist_reduce_fx) super__init__ isinstancefloatintr r add_stater)selfrr __class__s v/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/regression/minkowski.pyr"zMinkowskiDistance.__init__As[ "6"1ucl+Q'*hijhk(lm m +VC[QVWpredstargetsc\t|||j}|xj|z c_y)z*Update state with predictions and targets.N)r rr)r'r+r,rs r)updatezMinkowskiDistance.updateIs'7wO #55r*cBt|j|jS)zCompute metric.)r rr)r's r)computezMinkowskiDistance.computeNs*4+B+BDFFKKr*valaxc&|j||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import MinkowskiDistance >>> metric = MinkowskiDistance(p=3) >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import MinkowskiDistance >>> metric = MinkowskiDistance(p=3) >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )_plot)r'r1r2s r)plotzMinkowskiDistance.plotRsPzz#r""r*)NN)__name__ __module__ __qualname____doc__rrbool__annotations__rrrr$rrr"r.r0rrrrr5 __classcell__)r(s@r)rrs6)-x~,',htn,(-x~-!e!X%X3X4X6F6V66 LL _c(#E&(6*:":;<(#IQRZI[(# (#r*rN)collections.abcrtypingrrrtorchrr,torchmetrics.functional.regression.minkowskir r torchmetrics.metricr !torchmetrics.utilities.exceptionsr torchmetrics.utilities.importsr torchmetrics.utilities.plotrr__doctest_skip__rr r*r)rFs<%'' p&C@@01\#\#r*